---
title: "Storage Overview"
description: "Vector database components supporting multiple storage backends including Redis, MongoDB, Qdrant, and in-memory options"
icon: database
---

The Storage module provides comprehensive vector database support with multiple backend options, enabling you to choose the right storage solution for your performance and scalability requirements.

## Core Storage Components

Foundation components for vector storage management:

<CardGroup cols={1}>
  <Card 
    title="Vector Database" 
    icon="database" 
    href="/reference/dsl/storage/vector_database"
  >
    Base vector database interface and core functionality
  </Card>
</CardGroup>

## Vector Database Backends

Choose from multiple production-ready vector database backends:

<CardGroup cols={2}>
  <Card 
    title="Redis Vector Database" 
    icon="memory" 
    href="/reference/dsl/storage/redis_vector_database"
  >
    High-performance Redis-based vector storage
  </Card>
  <Card 
    title="MongoDB Vector Database" 
    icon="leaf" 
    href="/reference/dsl/storage/mongo_db_vector_database"
  >
    MongoDB vector search capabilities
  </Card>
  <Card 
    title="Qdrant Vector Database" 
    icon="vector-square" 
    href="/reference/dsl/storage/qdrant_vector_database"
  >
    Specialized Qdrant vector database integration
  </Card>
  <Card 
    title="In Memory Vector Database" 
    icon="microchip" 
    href="/reference/dsl/storage/in_memory_vector_database"
  >
    Fast in-memory storage for development and testing
  </Card>
</CardGroup>

<CardGroup cols={1}>
  <Card 
    title="TopK Vector Database" 
    icon="ranking-star" 
    href="/reference/dsl/storage/topk_vector_database"
  >
    Optimized top-K retrieval vector database
  </Card>
</CardGroup>

## Key Features

Storage components provide:

- **Multiple Backends**: Support for various vector database technologies
- **Performance Optimization**: Choose the right backend for your performance needs
- **Scalability**: From in-memory testing to distributed production storage
- **Compatibility**: Unified interface across all storage backends
- **Production Ready**: Battle-tested integrations with popular vector databases

<Info>
Each vector database backend is optimized for different use cases. Redis offers high performance, MongoDB provides rich querying, Qdrant specializes in vector search, and in-memory storage enables rapid development.
</Info>

## Backend Selection Guide

Choose the right vector database for your needs:

- **Redis**: Best for high-performance scenarios with frequent updates
- **MongoDB**: Ideal when you need rich metadata querying alongside vector search
- **Qdrant**: Optimized specifically for vector similarity search operations
- **In-Memory**: Perfect for development, testing, and small datasets
- **TopK**: Specialized for scenarios requiring only top-K nearest neighbor results

<Tip>
Start with in-memory storage during development for fast iteration, then choose a production backend based on your specific performance and scalability requirements.
</Tip>

## Storage Architecture

Storage components handle:

1. **Vector Indexing**: Efficient organization of high-dimensional vectors
2. **Similarity Search**: Fast nearest neighbor and similarity queries
3. **Metadata Management**: Storage and retrieval of associated metadata
4. **Performance Optimization**: Index tuning and query optimization 